论文标题
（1+1）EA的运行时分析转换线性函数的加权总和
Runtime Analysis of the (1+1) EA on Weighted Sums of Transformed Linear Functions
论文作者
论文摘要
线性函数在进化算法的运行时分析中起关键作用，研究为分析进化计算方法提供了广泛的新见解和技术。通过对可分离函数的研究和进化算法的优化行为以及来自机会约束优化领域的目标函数的优化行为，我们研究了两个转换线性函数的加权总和的目标函数类别。我们的结果表明，（1+1）EA的突变速率取决于功能重叠位的数量，在预期时间O（n log n）中为这些功能获得了最佳解决方案，从而将线性函数的众所周知结果推广到范围更大的问题。
Linear functions play a key role in the runtime analysis of evolutionary algorithms and studies have provided a wide range of new insights and techniques for analyzing evolutionary computation methods. Motivated by studies on separable functions and the optimization behaviour of evolutionary algorithms as well as objective functions from the area of chance constrained optimization, we study the class of objective functions that are weighted sums of two transformed linear functions. Our results show that the (1+1) EA, with a mutation rate depending on the number of overlapping bits of the functions, obtains an optimal solution for these functions in expected time O(n log n), thereby generalizing a well-known result for linear functions to a much wider range of problems.
